G = read.table("/Users/macbookpro/Documents/Bayesian Statistics/Project/Raw_data/Glicani/85 variabili/101_glicani-PreProcessed-IM-Step1-Step2-Step4-Step5-101.txt")
sum(is.na(G))
## [1] 863983
the numbers of na is substantial
vis_miss(G,warn_large_data = FALSE)
## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `gather()` instead.
## ℹ The deprecated feature was likely used in the visdat package.
## Please report the issue at <]8;;https://github.com/ropensci/visdat/issueshttps://github.com/ropensci/visdat/issues]8;;>.
the missing data is about 56%
skim(G)
| Name | G |
| Number of rows | 18239 |
| Number of columns | 84 |
| _______________________ | |
| Column type frequency: | |
| numeric | 84 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| X1007.23808835211 | 4520 | 0.75 | 0.31 | 0.07 | 0.09 | 0.26 | 0.31 | 0.36 | 0.87 | ▂▇▂▁▁ |
| X1023.21279242663 | 7819 | 0.57 | 0.28 | 0.07 | 0.09 | 0.23 | 0.27 | 0.32 | 1.57 | ▇▁▁▁▁ |
| X1054.36929659222 | 14105 | 0.23 | 0.32 | 0.10 | 0.07 | 0.24 | 0.31 | 0.38 | 1.18 | ▇▇▁▁▁ |
| X1080.63437109277 | 14464 | 0.21 | 0.22 | 0.06 | 0.07 | 0.17 | 0.21 | 0.25 | 0.50 | ▂▇▅▁▁ |
| X1095.67073368166 | 2607 | 0.86 | 1.74 | 0.35 | 0.32 | 1.51 | 1.74 | 1.96 | 3.85 | ▁▇▇▁▁ |
| X1096.67488974389 | 10583 | 0.42 | 0.82 | 0.17 | 0.20 | 0.70 | 0.82 | 0.93 | 1.63 | ▁▆▇▁▁ |
| X1097.67619211616 | 11430 | 0.37 | 0.37 | 0.10 | 0.10 | 0.30 | 0.37 | 0.44 | 0.80 | ▂▇▆▁▁ |
| X1105.25243608966 | 12066 | 0.34 | 0.38 | 0.10 | 0.13 | 0.31 | 0.38 | 0.45 | 0.74 | ▂▇▇▂▁ |
| X1111.66689339176 | 14272 | 0.22 | 0.87 | 0.20 | 0.25 | 0.74 | 0.87 | 1.00 | 2.15 | ▁▇▂▁▁ |
| X1120.38518614217 | 10435 | 0.43 | 0.47 | 0.17 | 0.10 | 0.35 | 0.44 | 0.56 | 2.39 | ▇▃▁▁▁ |
| X1121.30786937301 | 8404 | 0.54 | 0.37 | 0.10 | 0.13 | 0.31 | 0.36 | 0.41 | 3.07 | ▇▁▁▁▁ |
| X1122.33285552465 | 12671 | 0.31 | 0.25 | 0.08 | 0.06 | 0.20 | 0.24 | 0.29 | 1.37 | ▇▁▁▁▁ |
| X1136.36692738743 | 4533 | 0.75 | 0.75 | 0.27 | 0.10 | 0.57 | 0.71 | 0.90 | 3.64 | ▇▅▁▁▁ |
| X1137.35719872902 | 14339 | 0.21 | 0.51 | 0.17 | 0.12 | 0.39 | 0.50 | 0.61 | 2.08 | ▇▇▁▁▁ |
| X1150.62194430748 | 11766 | 0.35 | 0.41 | 0.10 | 0.12 | 0.34 | 0.40 | 0.47 | 0.84 | ▁▇▆▁▁ |
| X1158.36765531336 | 9931 | 0.46 | 0.35 | 0.08 | 0.09 | 0.29 | 0.35 | 0.40 | 0.71 | ▁▇▇▂▁ |
| X1159.35153465909 | 11443 | 0.37 | 0.23 | 0.06 | 0.07 | 0.19 | 0.23 | 0.27 | 0.86 | ▇▇▁▁▁ |
| X1160.34955263613 | 13974 | 0.23 | 0.23 | 0.06 | 0.08 | 0.19 | 0.22 | 0.27 | 0.54 | ▂▇▃▁▁ |
| X1172.67667121138 | 7420 | 0.59 | 0.39 | 0.10 | 0.12 | 0.32 | 0.38 | 0.45 | 0.82 | ▂▇▆▁▁ |
| X1174.34105674359 | 10339 | 0.43 | 0.33 | 0.08 | 0.11 | 0.27 | 0.33 | 0.38 | 0.66 | ▂▇▇▂▁ |
| X1194.71305441144 | 11794 | 0.35 | 0.25 | 0.07 | 0.07 | 0.20 | 0.24 | 0.29 | 0.57 | ▂▇▅▁▁ |
| X1257.76097042394 | 506 | 0.97 | 3.10 | 0.74 | 0.19 | 2.60 | 3.07 | 3.53 | 12.60 | ▃▇▁▁▁ |
| X1258.76390692027 | 3709 | 0.80 | 1.57 | 0.37 | 0.22 | 1.32 | 1.56 | 1.79 | 5.99 | ▃▇▁▁▁ |
| X1273.74592952337 | 19 | 1.00 | 32.47 | 7.26 | 0.13 | 27.85 | 32.07 | 36.57 | 79.37 | ▁▇▇▁▁ |
| X1274.74924191648 | 1833 | 0.90 | 17.10 | 3.46 | 0.05 | 14.82 | 16.97 | 19.18 | 41.30 | ▁▆▇▁▁ |
| X1275.74652836272 | 2540 | 0.86 | 8.81 | 1.88 | 0.53 | 7.60 | 8.72 | 9.89 | 21.81 | ▁▇▆▁▁ |
| X1298.7935513767 | 4106 | 0.77 | 1.18 | 0.24 | 0.17 | 1.01 | 1.17 | 1.33 | 2.80 | ▁▇▆▁▁ |
| X1299.78851832883 | 9112 | 0.50 | 0.68 | 0.15 | 0.10 | 0.57 | 0.67 | 0.78 | 1.66 | ▁▇▅▁▁ |
| X1312.70555522288 | 13642 | 0.25 | 0.20 | 0.05 | 0.08 | 0.16 | 0.19 | 0.23 | 0.42 | ▃▇▅▁▁ |
| X1314.77923020921 | 13926 | 0.24 | 0.28 | 0.08 | 0.09 | 0.22 | 0.27 | 0.33 | 0.60 | ▂▇▆▂▁ |
| X1339.81344700615 | 14501 | 0.20 | 0.26 | 0.08 | 0.08 | 0.20 | 0.25 | 0.30 | 0.60 | ▃▇▅▁▁ |
| X1355.81342269455 | 7856 | 0.57 | 1.02 | 0.22 | 0.25 | 0.87 | 1.01 | 1.16 | 2.40 | ▁▇▅▁▁ |
| X1356.81729427008 | 10858 | 0.40 | 0.60 | 0.14 | 0.09 | 0.50 | 0.59 | 0.68 | 1.35 | ▁▇▇▁▁ |
| X1357.81803786305 | 13276 | 0.27 | 0.33 | 0.09 | 0.06 | 0.26 | 0.32 | 0.38 | 0.72 | ▁▇▇▂▁ |
| X1397.8251087664 | 8165 | 0.55 | 0.28 | 0.09 | 0.06 | 0.21 | 0.27 | 0.33 | 0.69 | ▂▇▃▁▁ |
| X1398.83247690586 | 13534 | 0.26 | 0.19 | 0.06 | 0.05 | 0.15 | 0.18 | 0.23 | 0.46 | ▂▇▃▁▁ |
| X1419.83984925615 | 13224 | 0.27 | 1.43 | 0.36 | 0.05 | 1.19 | 1.42 | 1.64 | 3.64 | ▁▇▆▁▁ |
| X1435.82424478257 | 209 | 0.99 | 11.89 | 2.93 | 0.08 | 9.98 | 11.65 | 13.56 | 26.85 | ▁▅▇▁▁ |
| X1439.84952205937 | 13692 | 0.25 | 0.42 | 0.12 | 0.09 | 0.33 | 0.41 | 0.49 | 1.07 | ▂▇▃▁▁ |
| X1460.86756694855 | 12067 | 0.34 | 1.15 | 0.28 | 0.05 | 0.96 | 1.13 | 1.31 | 3.33 | ▁▇▂▁▁ |
| X1501.89575378187 | 4718 | 0.74 | 11.59 | 2.61 | 0.03 | 9.99 | 11.48 | 13.11 | 30.82 | ▁▇▅▁▁ |
| X1517.89380823532 | 14479 | 0.21 | 0.35 | 0.11 | 0.10 | 0.27 | 0.34 | 0.42 | 0.79 | ▂▇▆▁▁ |
| X1603.93285349585 | 14325 | 0.21 | 0.29 | 0.06 | 0.07 | 0.25 | 0.29 | 0.33 | 0.54 | ▁▅▇▂▁ |
| X1604.92692536332 | 13110 | 0.28 | 0.25 | 0.06 | 0.06 | 0.21 | 0.24 | 0.28 | 0.47 | ▁▆▇▂▁ |
| X1645.95404319726 | 14203 | 0.22 | 0.15 | 0.04 | 0.04 | 0.12 | 0.15 | 0.18 | 0.33 | ▁▇▅▁▁ |
| X1722.00370903208 | 14543 | 0.20 | 0.18 | 0.06 | 0.06 | 0.14 | 0.17 | 0.21 | 0.43 | ▃▇▃▁▁ |
| X1906.06159070316 | 3886 | 0.79 | 0.54 | 0.15 | 0.06 | 0.44 | 0.54 | 0.63 | 1.45 | ▁▇▃▁▁ |
| X1907.07384539805 | 9219 | 0.49 | 0.40 | 0.12 | 0.08 | 0.32 | 0.40 | 0.48 | 1.08 | ▂▇▃▁▁ |
| X1927.98890691122 | 2939 | 0.84 | 0.27 | 0.08 | 0.07 | 0.21 | 0.26 | 0.32 | 0.79 | ▃▇▂▁▁ |
| X1928.99148346869 | 8223 | 0.55 | 0.21 | 0.06 | 0.06 | 0.17 | 0.21 | 0.25 | 0.57 | ▃▇▂▁▁ |
| X1929.99255956031 | 10753 | 0.41 | 0.16 | 0.05 | 0.06 | 0.13 | 0.16 | 0.19 | 0.41 | ▃▇▃▁▁ |
| X1953.09258073233 | 12594 | 0.31 | 0.15 | 0.04 | 0.05 | 0.11 | 0.14 | 0.17 | 0.34 | ▃▇▃▁▁ |
| X1954.10473321313 | 13962 | 0.23 | 0.14 | 0.05 | 0.05 | 0.11 | 0.14 | 0.17 | 0.37 | ▅▇▂▁▁ |
| X1969.01093012276 | 12619 | 0.31 | 0.17 | 0.04 | 0.03 | 0.14 | 0.17 | 0.20 | 0.37 | ▁▇▇▂▁ |
| X1994.10302397621 | 9497 | 0.48 | 0.16 | 0.05 | 0.04 | 0.13 | 0.16 | 0.19 | 0.40 | ▂▇▃▁▁ |
| X1995.11794029212 | 10797 | 0.41 | 0.17 | 0.05 | 0.05 | 0.13 | 0.16 | 0.20 | 0.47 | ▃▇▂▁▁ |
| X1996.10637962176 | 14482 | 0.21 | 0.14 | 0.04 | 0.04 | 0.11 | 0.13 | 0.16 | 0.32 | ▂▇▅▁▁ |
| X2010.0707542606 | 13760 | 0.25 | 0.14 | 0.04 | 0.04 | 0.12 | 0.14 | 0.17 | 0.37 | ▂▇▃▁▁ |
| X2026.13411594976 | 14307 | 0.22 | 0.13 | 0.04 | 0.04 | 0.10 | 0.12 | 0.15 | 0.29 | ▃▇▅▁▁ |
| X2067.15534850027 | 13085 | 0.28 | 0.11 | 0.04 | 0.03 | 0.09 | 0.11 | 0.14 | 0.33 | ▅▇▂▁▁ |
| X2115.19056235604 | 8002 | 0.56 | 0.16 | 0.06 | 0.03 | 0.12 | 0.15 | 0.19 | 0.43 | ▃▇▃▁▁ |
| X2116.15804918102 | 9911 | 0.46 | 0.16 | 0.05 | 0.04 | 0.12 | 0.15 | 0.19 | 0.42 | ▃▇▃▁▁ |
| X2117.17812491318 | 13156 | 0.28 | 0.13 | 0.04 | 0.03 | 0.10 | 0.12 | 0.15 | 0.33 | ▃▇▃▁▁ |
| X2131.0904558004 | 5911 | 0.68 | 0.17 | 0.06 | 0.03 | 0.13 | 0.16 | 0.21 | 0.50 | ▃▇▂▁▁ |
| X2132.09867199741 | 9715 | 0.47 | 0.16 | 0.06 | 0.03 | 0.12 | 0.16 | 0.20 | 0.47 | ▃▇▂▁▁ |
| X2133.10458924582 | 12988 | 0.29 | 0.14 | 0.05 | 0.03 | 0.11 | 0.14 | 0.17 | 0.39 | ▃▇▃▁▁ |
| X2156.1782470825 | 14355 | 0.21 | 0.13 | 0.04 | 0.04 | 0.10 | 0.12 | 0.15 | 0.33 | ▃▇▃▁▁ |
| X2172.06080944893 | 7567 | 0.59 | 0.11 | 0.04 | 0.03 | 0.08 | 0.10 | 0.14 | 0.32 | ▅▇▃▁▁ |
| X2173.12167061973 | 13793 | 0.24 | 0.13 | 0.04 | 0.04 | 0.09 | 0.12 | 0.15 | 0.36 | ▅▇▂▁▁ |
| X2213.1691692717 | 6872 | 0.62 | 0.09 | 0.04 | 0.02 | 0.07 | 0.09 | 0.12 | 0.32 | ▇▇▂▁▁ |
| X2214.12776174634 | 10438 | 0.43 | 0.10 | 0.04 | 0.02 | 0.07 | 0.10 | 0.13 | 0.31 | ▅▇▃▁▁ |
| X2215.30474997573 | 14072 | 0.23 | 0.10 | 0.04 | 0.03 | 0.07 | 0.10 | 0.12 | 0.29 | ▆▇▂▁▁ |
| X2303.26341177395 | 13894 | 0.24 | 0.13 | 0.04 | 0.03 | 0.10 | 0.12 | 0.16 | 0.38 | ▃▇▂▁▁ |
| X2318.08302732164 | 2155 | 0.88 | 0.10 | 0.04 | 0.03 | 0.07 | 0.09 | 0.12 | 0.36 | ▇▆▂▁▁ |
| X2319.19463247548 | 6704 | 0.63 | 0.11 | 0.05 | 0.03 | 0.07 | 0.10 | 0.14 | 0.42 | ▇▆▁▁▁ |
| X2320.24835445304 | 12408 | 0.32 | 0.11 | 0.04 | 0.03 | 0.07 | 0.10 | 0.13 | 0.33 | ▇▇▂▁▁ |
| X2375.17934903393 | 13125 | 0.28 | 0.10 | 0.04 | 0.03 | 0.07 | 0.10 | 0.12 | 0.35 | ▇▇▂▁▁ |
| X2481.3079356675 | 14522 | 0.20 | 0.10 | 0.04 | 0.03 | 0.07 | 0.10 | 0.12 | 0.31 | ▆▇▂▁▁ |
| X2521.05312515152 | 13249 | 0.27 | 0.09 | 0.04 | 0.03 | 0.06 | 0.08 | 0.11 | 0.27 | ▇▇▂▁▁ |
| X2521.3660375501 | 13332 | 0.27 | 0.10 | 0.04 | 0.02 | 0.07 | 0.09 | 0.12 | 0.30 | ▅▇▂▁▁ |
| X2522.38008739363 | 13256 | 0.27 | 0.10 | 0.04 | 0.03 | 0.07 | 0.09 | 0.12 | 0.27 | ▆▇▃▁▁ |
| X2626.30195630964 | 5870 | 0.68 | 0.09 | 0.04 | 0.02 | 0.06 | 0.09 | 0.12 | 0.34 | ▇▆▁▁▁ |
| X2627.41870733666 | 13456 | 0.26 | 0.10 | 0.04 | 0.03 | 0.07 | 0.10 | 0.13 | 0.33 | ▇▇▂▁▁ |
| X2628.37443696087 | 14041 | 0.23 | 0.09 | 0.04 | 0.03 | 0.06 | 0.08 | 0.11 | 0.29 | ▇▇▂▁▁ |
we observe that the missing data is not uniform in the mz, there are some values for which only 20 - 30% of the pixel have a value, and this tends to be small
we replace the missing data with 0 since it means the data for that mz was under threshold
G0 = G
G0[is.na(G0)] = 0
cm <- cor(G0)
colnames(G0) = substr(colnames(G0),1,4)
corrplot(cm, method = "color", tl.pos = 'n')
the correlation is not high between the features, this is different from the Lipids
pixels = read.table("/Users/macbookpro/Documents/Bayesian Statistics/Project/Raw_data/Glicani/85 variabili/101_glicani-PreProcessed-XYCoordinates-Step1-Step2-Step4-Step5-101.txt")
colnames(G0) = substr(colnames(G0),1,4)
colnames(pixels) = c("x","y")
max_n_of_pixel = read.table("/Users/macbookpro/Documents/Bayesian Statistics/Project/Raw_data/Glicani/85 variabili/101_glicani-PreProcessed-maxXY-Step1-Step2-Step4-Step5-101.txt")
Data_long = as_tibble(data.frame( pixels, G0 ))
max_number_of_pixels = apply(Data_long[,1:2],2,max)
Data_array = matrix(NA,max_number_of_pixels[1],max_number_of_pixels[2])
Data_array = array(NA,c(max_number_of_pixels[1],max_number_of_pixels[2],ncol(G0)))
sum(is.na(G0))
## [1] 0
# there must be a better way to do this
for(k in 1:ncol(G0)){
for(i in 1:nrow(Data_long)){
Data_array[Data_long$x[i],Data_long$y[i],k] = G0[i,k]
}
}
dim(Data_array)
## x y
## 157 178 84
Data_very_long = reshape2::melt(Data_long,c("x","y")) %>% mutate(pixel_ind = paste0(x,"_",y), value_ind = rep(1:nrow(Data_long),ncol(G0)))
Data_very_long = Data_very_long %>% group_by(pixel_ind) %>% mutate(n = row_number()) %>% ungroup() %>% mutate(mz = as.numeric(substr(variable,2,4)))
Data_very_long = reshape2::melt(Data_long,c("x","y")) %>% mutate(pixel_ind = paste0(x,"_",y), value_ind = rep(1:nrow(Data_long),ncol(G0)))
Data_very_long = Data_very_long %>% group_by(pixel_ind) %>% mutate(n = row_number()) %>% ungroup() %>% mutate(mz = as.numeric(substr(variable,2,4)))
# subsampling to get a faster plot and not drain memory
sub_ind = sample(unique(Data_very_long$pixel_ind),1000)
# just to get the gist:
ggplot(Data_very_long %>% filter(pixel_ind %in% sub_ind))+
geom_path(aes(x = mz, y = value,
col=pixel_ind,
group = pixel_ind),alpha=.5)+theme_bw()+theme(legend.position = "none")+xlab("m.z")+scale_color_viridis_d(option = "A")+
scale_x_continuous(n.breaks = 20)
the first values are quite noisy
here we start seeing the spikes, they are for 125 a few spots the rest is quite uniform, for 127 we have it quite spread out
there are some holes in the data, the only structure is the one in 127
still quite noisy with some spots with high values
we can see some struture in 143 same struture as before
150 is another level where wee have features the rest is just noise
just noise and low values, the same holes repeats the next is still just noise
this is just noise up to the end of the available mz
it is the same peaks with some degraded points
there are this two strange patterns, the conentrates spikes anf the holes
pca = princomp(G0)
plot(pca)
summary(pca)
## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 9.4844228 5.1242938 4.1983528 3.03965996 1.47092223
## Proportion of Variance 0.6036961 0.1762238 0.1182917 0.06200784 0.01452031
## Cumulative Proportion 0.6036961 0.7799199 0.8982117 0.96021953 0.97473985
## Comp.6 Comp.7 Comp.8 Comp.9
## Standard deviation 0.657336674 0.645979110 0.610847759 0.552186490
## Proportion of Variance 0.002899828 0.002800487 0.002504163 0.002046294
## Cumulative Proportion 0.977639675 0.980440162 0.982944325 0.984990619
## Comp.10 Comp.11 Comp.12 Comp.13
## Standard deviation 0.516524757 0.498932917 0.473546948 0.443865738
## Proportion of Variance 0.001790519 0.001670632 0.001504952 0.001322208
## Cumulative Proportion 0.986781138 0.988451770 0.989956722 0.991278930
## Comp.14 Comp.15 Comp.16 Comp.17
## Standard deviation 0.3834690731 0.348093244 0.3359170408 0.2958763279
## Proportion of Variance 0.0009868639 0.000813182 0.0007572873 0.0005875124
## Cumulative Proportion 0.9922657942 0.993078976 0.9938362634 0.9944237758
## Comp.18 Comp.19 Comp.20 Comp.21
## Standard deviation 0.2259710597 0.2104915001 0.1919069696 0.1892836132
## Proportion of Variance 0.0003426906 0.0002973485 0.0002471599 0.0002404488
## Cumulative Proportion 0.9947664664 0.9950638149 0.9953109748 0.9955514236
## Comp.22 Comp.23 Comp.24 Comp.25
## Standard deviation 0.1858510617 0.1848481951 0.1825676413 0.180579729
## Proportion of Variance 0.0002318071 0.0002293121 0.0002236888 0.000218844
## Cumulative Proportion 0.9957832307 0.9960125428 0.9962362316 0.996455076
## Comp.26 Comp.27 Comp.28 Comp.29
## Standard deviation 0.1758785370 0.1657935510 0.1640670459 0.1608873772
## Proportion of Variance 0.0002075976 0.0001844726 0.0001806506 0.0001737163
## Cumulative Proportion 0.9966626731 0.9968471457 0.9970277963 0.9972015125
## Comp.30 Comp.31 Comp.32 Comp.33
## Standard deviation 0.1501372965 0.1478498431 0.1461086497 0.1378333163
## Proportion of Variance 0.0001512773 0.0001467028 0.0001432677 0.0001274985
## Cumulative Proportion 0.9973527898 0.9974994926 0.9976427603 0.9977702588
## Comp.34 Comp.35 Comp.36 Comp.37
## Standard deviation 0.1364428678 0.1304432382 0.1235310915 1.212386e-01
## Proportion of Variance 0.0001249391 0.0001141931 0.0001024116 9.864571e-05
## Cumulative Proportion 0.9978951979 0.9980093909 0.9981118025 9.982104e-01
## Comp.38 Comp.39 Comp.40 Comp.41
## Standard deviation 1.201414e-01 1.153015e-01 1.130472e-01 1.100120e-01
## Proportion of Variance 9.686829e-05 8.922088e-05 8.576614e-05 8.122255e-05
## Cumulative Proportion 9.983073e-01 9.983965e-01 9.984823e-01 9.985635e-01
## Comp.42 Comp.43 Comp.44 Comp.45
## Standard deviation 1.085717e-01 1.068849e-01 1.048044e-01 9.974492e-02
## Proportion of Variance 7.910974e-05 7.667065e-05 7.371493e-05 6.676951e-05
## Cumulative Proportion 9.986426e-01 9.987193e-01 9.987930e-01 9.988598e-01
## Comp.46 Comp.47 Comp.48 Comp.49
## Standard deviation 9.150374e-02 9.007107e-02 8.666905e-02 8.629786e-02
## Proportion of Variance 5.619197e-05 5.444615e-05 5.041092e-05 4.998004e-05
## Cumulative Proportion 9.989160e-01 9.989704e-01 9.990208e-01 9.990708e-01
## Comp.50 Comp.51 Comp.52 Comp.53
## Standard deviation 8.569246e-02 8.512367e-02 8.433057e-02 8.382657e-02
## Proportion of Variance 4.928126e-05 4.862921e-05 4.772727e-05 4.715849e-05
## Cumulative Proportion 9.991201e-01 9.991687e-01 9.992165e-01 9.992636e-01
## Comp.54 Comp.55 Comp.56 Comp.57
## Standard deviation 8.304057e-02 8.264791e-02 8.104304e-02 7.517147e-02
## Proportion of Variance 4.627828e-05 4.584166e-05 4.407863e-05 3.792299e-05
## Cumulative Proportion 9.993099e-01 9.993557e-01 9.993998e-01 9.994377e-01
## Comp.58 Comp.59 Comp.60 Comp.61
## Standard deviation 6.922338e-02 6.639589e-02 0.0661546426 6.463111e-02
## Proportion of Variance 3.215898e-05 2.958551e-05 0.0000293709 2.803366e-05
## Cumulative Proportion 9.994699e-01 9.994995e-01 0.9995288532 9.995569e-01
## Comp.62 Comp.63 Comp.64 Comp.65
## Standard deviation 6.422775e-02 6.270395e-02 6.166750e-02 6.055746e-02
## Proportion of Variance 2.768484e-05 2.638677e-05 2.552168e-05 2.461115e-05
## Cumulative Proportion 9.995846e-01 9.996110e-01 9.996365e-01 9.996611e-01
## Comp.66 Comp.67 Comp.68 Comp.69
## Standard deviation 5.892325e-02 5.769626e-02 5.751790e-02 5.713134e-02
## Proportion of Variance 2.330075e-05 2.234045e-05 2.220254e-05 2.190511e-05
## Cumulative Proportion 9.996844e-01 9.997067e-01 9.997289e-01 9.997508e-01
## Comp.70 Comp.71 Comp.72 Comp.73
## Standard deviation 5.599623e-02 5.498231e-02 5.459108e-02 5.392416e-02
## Proportion of Variance 2.104331e-05 2.028815e-05 2.000046e-05 1.951477e-05
## Cumulative Proportion 9.997719e-01 9.997922e-01 9.998122e-01 9.998317e-01
## Comp.74 Comp.75 Comp.76 Comp.77
## Standard deviation 5.381131e-02 5.309932e-02 5.235614e-02 4.978128e-02
## Proportion of Variance 1.943317e-05 1.892232e-05 1.839636e-05 1.663139e-05
## Cumulative Proportion 9.998511e-01 9.998700e-01 9.998884e-01 9.999051e-01
## Comp.78 Comp.79 Comp.80 Comp.81
## Standard deviation 0.0494976227 4.895491e-02 4.786210e-02 4.526676e-02
## Proportion of Variance 0.0000164424 1.608382e-05 1.537376e-05 1.375167e-05
## Cumulative Proportion 0.9999215125 9.999376e-01 9.999530e-01 9.999667e-01
## Comp.82 Comp.83 Comp.84
## Standard deviation 4.306283e-02 4.154729e-02 3.712239e-02
## Proportion of Variance 1.244519e-05 1.158463e-05 9.248439e-06
## Cumulative Proportion 9.999792e-01 9.999908e-01 1.000000e+00
we nee much more components to get a googe percentage of the variance than in the lipid case
PCA1 = ggplot(Data_long %>% mutate(pca1 = pca$scores[,1]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca1))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA2 = ggplot(Data_long %>% mutate(pca2 = pca$scores[,2]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca2))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA3 = ggplot(Data_long %>% mutate(pca1 = pca$scores[,3]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca1))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA4 = ggplot(Data_long %>% mutate(pca2 = pca$scores[4]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca2))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA5 = ggplot(Data_long %>% mutate(pca1 = pca$scores[,5]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca1))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA6 = ggplot(Data_long %>% mutate(pca2 = pca$scores[,6]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca2))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA1+PCA2+PCA3+PCA4+PCA5+PCA6
PCA1 = ggplot(Data_long %>% mutate(pca1 = pca$scores[,1]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca1))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA2 = ggplot(Data_long %>% mutate(pca2 = pca$scores[,2]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca2))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA3 = ggplot(Data_long %>% mutate(pca1 = pca$scores[,3]))+ theme_bw()+
geom_tile(aes(x=x,y=y,fill = pca1))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
P1 = ggplot(Data_long)+
geom_tile(aes(x=x,y=y,fill = X143))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
P2 = ggplot(Data_long)+
geom_tile(aes(x=x,y=y,fill = X127))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
P3 = ggplot(Data_long)+
geom_tile(aes(x=x,y=y,fill = X127.2))+scale_fill_viridis_c(option = "A",na.value = "red")+
theme_void()+theme(legend.position = "bottom")
PCA1+PCA2+PCA3+P1+P2+P3
this are the three clear peaks that one can see in the data